Extreme Gradient Boosting for Survival Analysis?

A bit of a theoretical question here. A colleague of mine created an XGBoost model to estimate the probability of a sale closing on a software deal. The deal is scored every day and updated when changes in the deal terms exist, etc. However the estimates are extremely unstable day to day. They have about a 5% standard deviation. Is this approach valid or should a semi-parametric model such as Cox proportional hazards be used because this is continuous in nature. Is there a way to utilize xgboost to estimate how likely a deal is to close within a month or quarter and score it daily?
I know people use some of these machine learning algorithms in survival analysis or use stacked ensembles. I haven't tried them for this. Mainly, since I was unsure if they were able to generate the quintessential survival plots, which are important to me. These algorithm do phenomenal jobs with prediction, but are normally blackboxes. I may still lean toward Cox regression due to its interpretability. I did not follow your last question. Are you asking if it can handle discrete time?